1. Spatial Data Mining
hari agung

2. What is Spatial Data? The data related to objects that occupy space
traffic, bird habitats, global climate, logistics, ...
Object types:
Points, Lines, Polygons,etc.
Used in/for:
GIS - Geographic Information Systems
Meteorology
Astronomy
Environmental studies, etc.

3. Why do we need Data Mining?
Large number of records(cases) ( bytes)
One thousand (103) bytes = 1 kilobyte (KB)
One million (106) bytes = 1 megabyte (MB)
One billion (109) bytes = 1 gigabyte (GB)
One trillion (1012) bytes = 1 terabyte (TB)
High dimensional data (variables)
attributes
Only a small portion, typically 5% to 10%, of the collected data is ever analyzed
We are drowning in data, but starving for knowledge!

4. Spatial Data Mining Spatial Patterns Primary Tasks Spatial outliers
Location prediction
Associations, co-locations
Hotspots, Clustering, trends, …
Primary Tasks
Mining Spatial Association Rules
Spatial Classification and Prediction
Spatial Data Clustering Analysis
Spatial Outlier Analysis

5. Spatial Classification
Use spatial information at different (coarse/fine) levels (different indexing trees) for data focusing
Determine relevant spatial or non-spatial features
Perform normal supervised learning algorithms
e.g., Decision trees,

6. Spatial Clustering Use tree structures to index spatial data
DBSCAN: R-tree
CLIQUE: Grid or Quad tree
Clustering with spatial constraints (obstacles  need to adjust notion of distance)

7. Spatial Association Rules
Spatial objects are of major interest, not transactions
A  B
A, B can be either spatial or non-spatial (3 combinations)
What is the fourth combination?
Association rules can be found w.r.t. the 3 types Pp

8. Spatial Data Mining Results
Understanding spatial data, discovering relationships between spatial and nonspatial data, construction of spatial knowledge bases, etc.
In various forms
The description of the general weather patterns in a set of geographic regions is a spatial characteristic rule.
The comparison of two weather patterns in two geographic regions is a spatial discriminant rule.
A rule like “most cities in Canada are close to the Canada-US border” is a spatial association rule
near(x,coast) ^ southeast(x, USA) ) hurricane(x), (70%)
Others: spatial clusters,…

9. Basic Concepts (1)
Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography, meteorology, etc.
The main difference (Spatial autocorrelation)
the neighbors of a spatial object may have an influence on it and therefore have to be considered as well
Spatial attributes
Topological
adjacency or inclusion information
Geometric
position (longitude/latitude), area, perimeter, boundary polygon

10. Basic Concepts (2) Spatial neighborhood
Topological relation
“intersect”, “overlap”, “disjoint”, …
distance relation
“close_to”, “far_away”,…
direction/orientation relation
“left_of”, “west_of”,…
Global model might be inconsistent with regional models
Global Model
Local Model

11. Applications NASA Earth Observing System (EOS): Earth science data
National Inst. of Justice: crime mapping
Census Bureau, Dept. of Commerce: census data
Dept. of Transportation (DOT): traffic data
National Inst. of Health(NIH): cancer clusters

12. Example: What Kind of Houses Are Highly Valued
Example: What Kind of Houses Are Highly Valued?—Associative Classification

13. Data SOM Application for DataMining Downscaling Weather Forecasts
ERA-15 using a T106L31 model (from 1978 to 1994) with 1.125◦ resolution
Terabytes
Comprises data from approx. 20 variables (such as temperature,humidity, pressure, etc.) at 30 pressure levels of a 360x360 nodes grid 6
SOM Application for DataMining
Downscaling Weather Forecasts
Adaptive
Competitive
Learning
Sub-grid details scape from numerical models

14. Dept. of Applied Mathematics
Universidad de Cantabria
Santander, Spain

15. And now discussion